Proportion of Physicians Who Treat Patients With Greater Social and Clinical Risk and Physician Inclusion in Medicare Advantage Networks

Key Points Question Are clinicians who are treating greater numbers of patients with more social and clinical risk factors in traditional Medicare less likely to be included in Medicare Advantage (MA) plan networks? Findings In this cross-sectional study of 259 932 clinicians participating in Medicare in 2019, those at the highest quintiles for patients who were dually eligible for Medicare and Medicaid (a proxy measure of social risk) and patients’ hierarchical condition category scores (a proxy measure of clinical risk) were associated with a significantly lower likelihood of being included in MA plan networks and being in network with MA enrollees than those at the lowest such quintiles. Meaning Physicians with the highest proportion of patients who were dually eligible for Medicare and Medicaid and with the highest hierarchical condition category scores within traditional Medicare were associated with a significantly lower likelihood of being included in MA networks.


Introduction
The Medicare Advantage (MA) program is a private insurance alternative to the traditional Medicare (TM) program that has been expanding rapidly in the past decade. 1 The MA program enrollment has more than doubled from 11 million beneficiaries in 2010 to 24 million in 2020 and now enrolls 50% of Medicare beneficiaries. 1 Medicare Advantage plans are required to provide at least the same services covered by TM but differ in that they can limit their enrollees to a specific network of physicians to control costs and improve quality of care under their capitated payments. 2 It is not currently known how plans make decisions about what physicians to include in network, and if there are differences in the patients for whom the included physicians care.
There is limited prior evidence on how plans design networks in the MA program. Prior work has found that many MA plans may implement narrow network designs for physician and professional services in Part B. More than a third of MA plans included fewer than 30% of physicians in their contracted counties. 3 Fewer than 60% of primary care physicians and fewer than 20% of mental and behavioral health clinicians were included in any MA plans in 2019. 4 At the same time, other work has found that narrow networks may be associated with improvements in plan quality. 5 Few studies have examined what role physician characteristics play in whether that physician is included in MA plan networks.
Medicare Advantage plan networks are crucial in providing accessible care, but the factors associated with the inclusion of physicians in MA networks are unknown. To maximize risk-adjusted per-enrollee capitated payments, MA plans may limit networks to physicians treating lower numbers of patients with more social and clinical risks who may be viewed as unprofitable. In this study, we sought to assess the association between a physician's inclusion in MA networks and the number of their patients with social and clinical risks enrolled in TM Part B. Using data on physicians in medical specialties, primary care, and surgical specialties, we aimed to understand (1) whether physicians treating higher numbers of patients with greater social risks in TM are less likely to be included in MA networks, (2) whether physicians treating higher numbers of clinically complex patients in TM are less likely to be included in MA networks, and (3) whether these associations vary by physician specialties and practice locations.

Physician Sample
In this cross-sectional study, the primary study sample was all physicians who treated any TM beneficiaries in Part B in 2019. We used the 2019 Physician Compare data set to identify 3 types of physicians billing Medicare Part B based on their primary specialties 6 : (1) medical specialties (cardiology, endocrinology, gastroenterology, infectious disease, hematology, nephrology, oncology, pulmonology, and rheumatology), 7 (2) primary care (general practice, family practice, and internal medicine), 4 and (3) surgical specialties (general surgery, thoracic surgery, colon and rectal surgery, obstetrics and gynecology, neurological surgery, ophthalmology, orthopedic surgery, hand surgery, otolaryngology, plastic and reconstructive surgery, oral and maxillofacial surgery, urology, and vascular surgery). 8 Nonphysician clinicians were excluded from this study. At the National Provider Identifier (NPI) level, we collected physician sex, physician specialty, physician practice zip code, and year of graduation from medical school. We used the 2019 Medicare Provider Utilization and Payment Data file to collect the number of unique Part B beneficiaries treated, mean beneficiary age, the number of beneficiaries who were dually eligible for Medicare and Medicaid, and mean beneficiary HCC score for each physician. We merged the 2 data sets at the NPI level. Using the definition by the Federal Office of Rural Health Policy, 9 we determined the rurality of physician practice using the practice zip code. Using the US Department of Housing and Urban county-zip code crosswalk file, 10 we assigned the physician practice county using the practice zip code. We removed duplicate counties for a given physician practice zip code. We calculated the length of physician clinical practice as the difference between 2019 and the year of graduation from medical school. We calculated the percentage of patients with dual eligibility as the proportion of Part B beneficiaries  11 We linked these data to publicly available MA plan characteristics files, which included characteristics such as premium and plan rating. We used MA service area files to define in which counties each MA contract is certified to be offered. This study was determined to be exempt by the  14 The HCC score was originally designed to adjust capitation payments to MA plans according to beneficiary health risks. 14, 15 We operationalized both variables as quintiles of a physician's patients with dual eligibility and their mean HCC scores.

Statistical Analysis
First, we compared the characteristics of physicians across the quintiles of patients with dual eligibility and quintiles of mean beneficiary HCC score. To better understand the rates of inclusion across the distribution of patient dual eligibility and HCC score, we graphed the proportions of in-network MA plans and MA enrollees stratified by each specialty type of physician. Next, using data at the NPI physician level, we fit 2 primary model specifications.

Social Risk Model
In the first social risk model, the primary exposure of interest was the physician's quintile of patients with dual eligibility. We estimated the model using linear regression for each of the 2 outcomes (MA network inclusion rate and MA enrollee proportion) and adjusted for physician practice rurality, gender, specialty, years of service, number of unique beneficiaries, mean beneficiary age, and, importantly, the mean beneficiary level HCC score. We also included physician practice county fixed effects to more directly compare physicians who practiced in the same location with one another and to adjust for market supply of physicians and county MA penetration rate.

Clinical Risk Model
In the second clinical risk model, the primary exposure of interest was the quintile of patients' HCC scores per physician. We also estimated this model using linear regression and included a similar set of covariates (for physician practice rurality, gender, specialty, years of service, number of unique beneficiaries, mean beneficiary age) and practice county fixed effects. Differently from the social risk model, we also adjust for the proportion of beneficiaries who were dually eligible for Medicaid as an adjustment variable.
After estimating the social risk and clinical risk models for both outcomes, we conducted further analysis stratifying each model by specialty type to compare if network decisions appear to be made differently for different types of physicians. Second, we stratified by rurality, as network adequacy standards may be more binding in rural areas. Third, because variables, such as years of practice and rurality, may be mediators of the association of interest, we estimated additional models excluding these controls. Fourth, we tested the inclusion of physicians with different minimum counts of beneficiaries to see how sensitive the results were to the 100-patient cutoff we used. Last, we also

Results
The final analysis cohort included 259 932 physicians. A sample inclusion flowchart is in eFigure 2 in

Discussion
This cross-sectional study has 3 key findings. First, physicians who treat a higher proportion of patients dually eligible for Medicare and Medicaid in TM are substantially less likely to be included as in-network physicians in MA and serve MA beneficiaries. Second, physicians, especially those in medical specialties, treating patients with high clinical risk scores in TM are less likely to be included in MA plan networks and be in network with MA enrollees. Notably, this trend differed from primary care physicians where the differences in inclusion were much smaller. Third, these findings were amplified among physicians of medical specialties and those who practice in rural areas. These findings provide the first evidence that MA plans may limit networks to physicians treating TM The MA penetration among older adult patients with dual eligibility has historically lagged due to the lack of support from states and managed care. 21 However, a recent study suggests that MA networks are expanding to include more patients with dual eligibility in recent years. 22 From 2009 to 2018, dual enrollees were one of the most rapidly growing populations in MA. 22 Similarly, prior work has found that the inclusion of patients with dual eligibility in MA plans has increased over the past 10 years, and many MA plans are launching dual-eligible special needs plans (D-SNPs), a special type of plan that aims to manage the care of patients with dual eligibility. However, the present study finds that despite this growth in the number of patients with dual eligibility, MA plans do not seem to be networking with physicians who treat higher proportions of patients with dual eligibility in TM.
If these physicians are more experienced in the care of patients with complex medical and social needs, this could limit the promise of plans such as D-SNPs. Additional work is imperative to determine why physicians treating more socially disadvantaged patients in TM may be excluded from MA networks. We found that these results are primarily driven by a differential selection of medical specialty physicians across the distribution of patients with dual eligibility and that the differences were much smaller or nonsignificant for primary care physicians. This could be explained by plans selecting primary care physicians who care for patients with more socially and clinically complex patients as beneficiaries with dual eligibility are paid more in risk adjustment. A plan may profit from these riskadjusted payments if they are then able to use utilization control to keep their spending low. This may differ from medical specialty physicians with higher numbers of patients with dual eligibility, which may have more complicated outcomes without the added benefit of bringing more beneficiaries into the plan. Further research into network design should continue to differentiate between primary care and specialty physician dynamics.
The HCC score is a risk-adjustment tool designed by CMS to adjust capitation payments to MA plans according to beneficiary health risks and costs. 14 networks. An additional potential explanation is that patients with low socioeconomic status may have access to lower-quality physicians, and if lower-quality physicians are excluded from MA plans at higher rates, it could also lead to the present study's findings. In this descriptive analysis, we are not able to differentiate the causal direction of these associations.

Limitations
First, this study was cross-sectional, and the results cannot assess the causality of patient social and clinical risks in the MA inclusion rate. Second, we used TM Part B data to measure Medicareparticipating physician and beneficiary characteristics aggregated at the physician level and compared this with MA county-level plan and contract data. However, the risk profile of physicians' TM Part B vs MA patients may differ. Third, the methods assigned physicians to only a single practice county and may not fully reflect physicians who practice in multiple counties or physicians who do not keep their NPI registry updated. Fourth, Ideon data did not include the entire 2019 MA physician network data. However, as almost 90% of all MA enrollees were included in the data set, we were able to analyze data on most of the MA enrollees. 4,11 Fifth, the Ideon data, while the most commonly used file for network analysis in MA research, is still based on the directories reported by MA plans and may not fully represent the true access to in-network physicians. However, given that this analysis is focused on the physician level, the Ideon data still provide a valuable ability to compare physician-level inclusion in networks. Sixth, as the exposure variables are based on TM data, we are unable to compare outcomes for physicians who only treat MA beneficiaries; however, most physicians who treat MA beneficiaries likely also treat TM beneficiaries.

Conclusions
In this cross-sectional study, we found that physicians who treat more patients with clinical complexity and dual eligibility in Part B in the TM program were less likely to be included in many MA plan networks. This gap regarding network inclusion may result in physicians who are already underresourced being excluded from plans at higher rates. Alternatively, MA plans may be less attractive to these physicians. As MA plan penetration, particularly among enrollees with dual eligibility, increases in coming years, it will be imperative to ensure that patients have access to physicians who can address their care needs.